def CAOSpy_run(tstart,tstop,mc,pdyn,particles,leftover,drained):
timenow=tstart
#loop through time
while timenow < tstop:
print 'time:',timenow
[thS,npart]=pdyn.gridupdate_thS(particles.lat,particles.z,mc)
#define dt as Curant criterion
dt_D=(mc.mgrid.vertfac.values[0])**2 / (2*np.amax(mc.D[np.amax(thS),:]))
dt_ku=-mc.mgrid.vertfac.values[0]/np.amax(mc.ku[np.amax(thS),:])
dt=np.amin([dt_D,dt_ku])
#INFILT
p_inf=cinf.pmx_infilt(timenow,precTS,mc,dt,leftover)
#print timenow
#print p_inf
particlesnow=pd.concat([particles,p_inf])
#p_backup=particlesnow.copy()
#DIFFUSION
[particlesnow,thS,npart,phi_mx]=pdyn.part_diffusion_split(particlesnow,npart,thS,mc,dt,True,10)
#ADVECTION
particlesnow=pdyn.mac_advection(particlesnow,mc,thS,dt)
#drained particles
drained=drained.append(particlesnow[particlesnow.flag==len(mc.maccols)+1])
particlesnow=particlesnow[particlesnow.flag!=len(mc.maccols)+1]
#MX-MAC-INTERACTION
pdyn.mx_mp_interact(particlesnow,npart,thS,mc,dt)
pondparts=(particlesnow.z<0.)
leftover=np.count_nonzero(-pondparts)
particles=particlesnow[pondparts]
timenow=timenow+dt
return(particles,npart,thS,leftover,drained,timenow)
def CAOSpy_run(tstart,tstop,dt,mc,pdyn,particles,leftover,drained):
xstart=np.int64(tstart/dt)
xstop=np.int64(tstop/dt)
#loop through time
for t in np.arange(xstop-xstart)+xstart:
timenow=t*dt
print 'time:',timenow
[thS,npart]=pdyn.gridupdate_thS(particles.lat,particles.z,mc)
#INFILT
p_inf=cinf.pmx_infilt(timenow,precTS,mc,dt,leftover)
#print timenow
#print p_inf
particlesnow=pd.concat([particles,p_inf])
#p_backup=particlesnow.copy()
#DIFFUSION
[particlesnow,thS,npart,phi_mx]=pdyn.part_diffusion_split(particlesnow,npart,thS,mc,dt,True,10)
#ADVECTION
#particlesnow=pdyn.mac_advection(particlesnow,mc,thS,dt)
#drained particles
drained=drained.append(particlesnow[particlesnow.flag==len(mc.maccols)+1])
particlesnow=particlesnow[particlesnow.flag!=len(mc.maccols)+1]
#MX-MAC-INTERACTION
pdyn.mx_mp_interact(particlesnow,npart,thS,mc,dt)
pondparts=(particlesnow.z<0.)
leftover=np.count_nonzero(-pondparts)
particles=particlesnow[pondparts]
return(particles,npart,thS,leftover,drained,timenow)
def CAOSpy_rundx_noise(tstart,tstop,mc,pdyn,cinf,precTS,particles,leftover,drained,dt_max=1.,splitfac=10,prec_2D=False,maccoat=10.,exfilt_method='Ediss',saveDT=True,vertcalfac=1.,latcalfac=1.,clogswitch=False,infilt_method='MDA',film=True,dynamic_pedo=True,ksnoise=1.):
if run_from_ipython():
from IPython import display
timenow=tstart
prec_part=0. #precipitation which is less than one particle to accumulate
acc_mxinf=0. #matrix infiltration may become very small - this shall handle that some particles accumulate to infiltrate
exfilt_p=0. #exfiltration from the macropores
s_red=0.
#loop through time
while timenow < tstop:
[thS,npart]=pdyn.gridupdate_thS(particles.lat,particles.z,mc)
if saveDT==True:
#define dt as Courant/Neumann criterion
dt_D=(mc.mgrid.vertfac.values[0])**2 / (6*np.nanmax(mc.D[np.amax(thS),:]))
dt_ku=-mc.mgrid.vertfac.values[0]/np.nanmax(mc.ku[np.amax(thS),:])
dt=np.amin([dt_D,dt_ku,dt_max,tstop-timenow])
else:
if type(saveDT)==float:
#define dt as pre-defined
dt=np.amin([saveDT,tstop-timenow])
elif type(saveDT)==int:
#define dt as modified Corant/Neumann criterion
dt_D=(mc.mgrid.vertfac.values[0])**2 / (6*np.nanmax(mc.D[np.amax(thS),:]))*saveDT
dt_ku=-mc.mgrid.vertfac.values[0]/np.nanmax(mc.ku[np.amax(thS),:])*saveDT
dt=np.amin([dt_D,dt_ku,dt_max,tstop-timenow])
#INFILTRATION
[p_inf,prec_part,acc_mxinf]=cinf.pmx_infilt(timenow,precTS,prec_part,acc_mxinf,thS,mc,pdyn,dt,0.,prec_2D,particles.index[-1],infilt_method) #drain all ponding // leftover <-> 0.
particles=pd.concat([particles,p_inf])
#DIFFUSION
[particles,thS,npart,phi_mx]=pdyn.part_diffusion_split(particles,npart,thS,mc,dt,False,splitfac,vertcalfac,latcalfac,dynamic_pedo=True,ksnoise=ksnoise)
#ADVECTION
if not particles.loc[(particles.flag>0) & (particles.flag<len(mc.maccols)+1)].empty:
[particles,s_red,exfilt_p]=pdyn.mac_advection(particles,mc,thS,dt,clogswitch,maccoat,exfilt_method,film=film,dynamic_pedo=True,ksnoise=ksnoise)
#INTERACT
particles=pdyn.mx_mp_interact_nobulk(particles,npart,thS,mc,dt,dynamic_pedo=True,ksnoise=ksnoise)
if run_from_ipython():
display.clear_output()
display.display_pretty(''.join(['time: ',str(timenow),'s | precip: ',str(len(p_inf)),' particles | mean v(adv): ',str(particles.loc[particles.flag>0,'advect'].mean()),' m/s | exfilt: ',str(int(exfilt_p)),' particles']))
else:
print 'time: ',timenow,'s'
#CLEAN UP DATAFRAME
drained=drained.append(particles[particles.flag==len(mc.maccols)+1])
particles=particles[particles.flag!=len(mc.maccols)+1]
pondparts=(particles.z<0.)
leftover=np.count_nonzero(-pondparts)
particles.cell[particles.cell<0]=mc.mgrid.cells.values
particles=particles[pondparts]
timenow=timenow+dt
return(particles,npart,thS,leftover,drained,timenow)
def CAOSpy_rundx1(tstart,
tstop,
mc,
pdyn,
cinf,
precTS,
particles,
leftover=0,
drained=0.1,
dt_max=1.,
splitfac=10,
prec_2D=False,
maccoat=10.,
exfilt_method='Ediss',
saveDT=True,
vertcalfac=1.,
latcalfac=1.,
clogswitch=False,
infilt_method='MDA',
film=True,
infiltscale=False,
dynamic_pedo=False,
counteractpow=3):
if run_from_ipython():
from IPython import display
timenow = tstart
prec_part = 0. #precipitation which is less than one particle to accumulate
acc_mxinf = 0. #matrix infiltration may become very small - this shall handle that some particles accumulate to infiltrate
exfilt_p = 0. #exfiltration from the macropores
s_red = 0.
mc.splitfac = splitfac
mc.counteract_pow = counteractpow
if type(drained) == float:
drained = pd.DataFrame(np.array([]))
#loop through time
while timenow < tstop:
[thS, npart] = pdyn.gridupdate_thS(particles.lat, particles.z, mc)
if saveDT == True:
#define dt as Courant/Neumann criterion
dt_D = (mc.mgrid.vertfac.values[0])**2 / (
6 * np.amax(mc.D[particles.LTEbin.max(), :]))
dt_ku = -mc.mgrid.vertfac.values[0] / np.amax(
mc.ku[particles.LTEbin.max(), :])
dt = np.amin([dt_D, dt_ku, dt_max, tstop - timenow])
dt = np.amax([dt, 0.5])
mc.dt = dt
else:
if type(saveDT) == float:
#define dt as pre-defined
dt = np.amin([saveDT, tstop - timenow])
mc.dt = dt
elif type(saveDT) == int:
#define dt as modified Corant/Neumann criterion
dt_D = (mc.mgrid.vertfac.values[0])**2 / (
6 * np.nanmax(mc.D[np.amax(thS), :])) * saveDT
dt_ku = -mc.mgrid.vertfac.values[0] / np.nanmax(
mc.ku[np.amax(thS), :]) * saveDT
dt = np.amin([dt_D, dt_ku, dt_max, tstop - timenow])
mc.dt = dt
pcount = len(particles)
#INFILTRATION (new version with binned)
[p_inf, prec_part, acc_mxinf
] = cinf.pmx_infilt(timenow, precTS, prec_part, acc_mxinf, thS, mc,
pdyn, mc.dt, leftover, prec_2D,
particles.index[-1], infilt_method, infiltscale,
npart) #drain all ponding // leftover <-> 0.
particles = pd.concat([particles, p_inf])
#DEBUG:
#if len(particles)<100000:
# particles.to_pickle('DEBUG_particlesX.pic')
# assert False
#particles.to_pickle('DEBUG_particles.pic')
#particles=particles.groupby('cell').apply(pdyn.binupdate_pd,mc)
#particles.loc[particles.LTEbin>(len(mc.ku)-1),'LTEbin']=len(mc.ku)-1
#plot_mac(particles,'./results/Mtest'+str(np.round(timenow,2)))
#DIFFUSION
#[particles,thS,npart,phi_mx]=pdyn.part_diffusion_split(particles,npart,thS,mc,dt,False,splitfac,vertcalfac,latcalfac)
#[particles,thS,npart,phi_mx]=pdyn.part_diffusion_binned(particles,npart,thS,mc,dt)
[particles, thS, npart,
phi_mx] = pdyn.part_diffusion_binned_pd(particles, npart, thS, mc)
#ADVECTION
if not particles.loc[(particles.flag > 0)
& (particles.flag < len(mc.maccols) + 1)].empty:
[particles, s_red, exfilt_p,
mc] = pdyn.mac_advectionX(particles,
mc,
thS,
mc.dt,
clogswitch,
maccoat,
exfilt_method,
film=film,
dynamic_pedo=dynamic_pedo,
npart=npart,
fc_check=False)
#INTERACT
particles = pdyn.mx_mp_interact_nobulk(particles, npart, thS, mc,
mc.dt, dynamic_pedo)
#CLEAN UP DATAFRAME
drained = drained.append(particles[particles.flag == len(mc.maccols) +
1])
particles = particles.loc[particles.flag != len(mc.maccols) + 1]
#leftover+=np.intp((particles.z>=0.).sum())
leftover = particles.z[particles.z >= 0.].count()
particles = particles.loc[
particles.z < 0.] #drop all leftover back to infilt routine
if run_from_ipython():
display.clear_output()
display.display_pretty(''.join([
'time: ',
str(timenow), 's | n_particles: ',
str(pcount), ' | precip: ',
str(len(p_inf)), ' | advect: ',
str(
np.sum(((particles.flag > 0) &
(particles.flag <= len(mc.maccols))))),
' | exfilt: ',
str(int(exfilt_p)), ' | mean v(adv): ',
str(particles.loc[((particles.flag > 0) &
(particles.flag <= len(mc.maccols))),
'advect'].mean())[:7] +
str(particles.loc[((particles.flag > 0) &
(particles.flag <= len(mc.maccols))),
'advect'].mean())[-4:], ' m/s'
]))
display.display_pretty(''.join([
'time: ',
str(timenow), 's | n_particles now: ',
str(len(particles)), ' | leftover: ',
str(leftover)
]))
else:
print('time: ', timenow, 's')
#particles.loc[particles.cell<0,'cell']=mc.mgrid.cells.values
#particles=particles[pondparts]
timenow = timenow + mc.dt
return (particles, npart, thS, leftover, drained, timenow)
def CAOSpy_rund_diffonly(tstart,
tstop,
mc,
pdyn,
cinf,
precTS,
particles,
leftover,
drained,
dt_max=1.,
splitfac=10,
prec_2D=False,
saveDT=True,
vertcalfac=1.,
latcalfac=1.):
if run_from_ipython():
from IPython import display
timenow = tstart
prec_part = 0. #precipitation which is less than one particle to accumulate
acc_mxinf = 0. #matrix infiltration may become very small - this shall handle that some particles accumulate to infiltrate
exfilt_p = 0. #exfiltration from the macropores
s_red = 0.
#loop through time
while timenow < tstop:
[thS, npart] = pdyn.gridupdate_thS(particles.lat, particles.z, mc)
if saveDT == True:
#define dt as Courant/Neumann criterion
dt_D = (mc.mgrid.vertfac.values[0])**2 / (
6 * np.nanmax(mc.D[np.amax(thS), :]))
dt_ku = -mc.mgrid.vertfac.values[0] / np.nanmax(
mc.ku[np.amax(thS), :])
dt = np.amin([dt_D, dt_ku, dt_max, tstop - timenow])
else:
if type(saveDT) == float:
#define dt as pre-defined
dt = np.amin([saveDT, tstop - timenow])
elif type(saveDT) == int:
#define dt as modified Corant/Neumann criterion
dt_D = (mc.mgrid.vertfac.values[0])**2 / (
6 * np.nanmax(mc.D[np.amax(thS), :])) * saveDT
dt_ku = -mc.mgrid.vertfac.values[0] / np.nanmax(
mc.ku[np.amax(thS), :]) * saveDT
dt = np.amin([dt_D, dt_ku, tstop - timenow])
#INFILTRATION
[p_inf, prec_part, acc_mxinf] = cinf.pmx_infilt(
timenow, precTS, prec_part, acc_mxinf, thS, mc, pdyn, dt, 0.,
prec_2D,
particles.index[-1]) #drain all ponding // leftover <-> 0.
p_inf.flag = 0
particles = pd.concat([particles, p_inf])
#DIFFUSION
[particles, thS, npart,
phi_mx] = pdyn.part_diffusion_split(particles, npart, thS, mc, dt,
False, splitfac, vertcalfac,
latcalfac)
if run_from_ipython():
display.clear_output()
display.display_pretty(''.join([
'time: ',
str(timenow), 's | precip: ',
str(len(p_inf)), ' particles'
]))
else:
print 'time: ', timenow, 's'
#CLEAN UP DATAFRAME
drained = drained.append(particles[particles.flag == len(mc.maccols) +
1])
particles = particles[particles.flag != len(mc.maccols) + 1]
pondparts = (particles.z < 0.)
leftover = np.count_nonzero(-pondparts)
particles.cell[particles.cell < 0] = mc.mgrid.cells.values
particles = particles[pondparts]
timenow = timenow + dt
return (particles, npart, thS, leftover, drained, timenow)